Bone Regeneration in Athymic Calvarial Defects With Accell DBM100
Bibliographic record
Abstract
Bioimplants containing bone morphogenetic proteins (BMP) such as demineralized bone matrix (DBM) are used clinically to repair bone defects because of their ability to stimulate bone regeneration. Because of handling issues, DBM granules are often combined with an inert carrier, which reduces the DBM content to 40% or less by volume. Recently, Accell DBM100 (Accell, IsoTis OrthoBiologics, Irvine, CA) has been developed, which uses processed DBM as the carrier, resulting in a DBM content of 100%. The purpose of this investigation was to evaluate the use of Accell for bone defect healing.Forty-two athymic male rats were divided into three groups. Bilateral 5 mm calvarial defects were created in each animal. In group 1, one defect was filled with Accell and the other defect was left unfilled (control). In group 2, one defect was filled with OP-1 putty (recombinant human BMP-7 and type I collagen), and the other was left unfilled. In group 3, one defect was filled with Accell and the other with OP-1. Animals were sacrificed at 4 and 8 weeks, postoperatively. Specimens were analyzed by histomorphometry to evaluate bone regeneration quantitatively. Accell and OP-1 both induced significantly more bone at 4 and 8 weeks compared with the unfilled contralateral defects. OP-1-filled defects produced significantly more total reparative tissue (bone + marrow) compared with Accell (P < 0.01); however, the increase in new bone did not reach significance at either time (P = 0.06 at 4 wk; P = 0.10 at 8 wk). In conclusion, these results suggest that Accell DBM100 will be useful in repairing craniofacial bone defects clinically.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".